Overview

Brought to you by YData

Dataset statistics

Number of variables23
Number of observations3961
Missing cells7357
Missing cells (%)8.1%
Duplicate rows129
Duplicate rows (%)3.3%
Total size in memory2.4 MiB
Average record size in memory622.7 B

Variable types

Categorical10
Text3
Numeric10

Alerts

Dataset has 129 (3.3%) duplicate rowsDuplicates
area is highly overall correlated with bathroom and 4 other fieldsHigh correlation
bathroom is highly overall correlated with area and 4 other fieldsHigh correlation
bedRoom is highly overall correlated with area and 5 other fieldsHigh correlation
built_up_area is highly overall correlated with carpet_area and 2 other fieldsHigh correlation
carpet_area is highly overall correlated with area and 5 other fieldsHigh correlation
facing is highly overall correlated with built_up_areaHigh correlation
price is highly overall correlated with area and 6 other fieldsHigh correlation
price_per_sqft is highly overall correlated with priceHigh correlation
property_type is highly overall correlated with bedRoom and 2 other fieldsHigh correlation
servant room is highly overall correlated with super_built_up_areaHigh correlation
super_built_up_area is highly overall correlated with area and 7 other fieldsHigh correlation
store room is highly imbalanced (56.0%) Imbalance
facing has 1177 (29.7%) missing values Missing
super_built_up_area has 2027 (51.2%) missing values Missing
built_up_area has 2113 (53.3%) missing values Missing
carpet_area has 1958 (49.4%) missing values Missing
area is highly skewed (γ1 = 59.61688041) Skewed
built_up_area is highly skewed (γ1 = 42.53644152) Skewed
carpet_area is highly skewed (γ1 = 25.1682128) Skewed
floorNum has 139 (3.5%) zeros Zeros
luxury_score has 559 (14.1%) zeros Zeros

Reproduction

Analysis started2025-01-20 17:08:17.387695
Analysis finished2025-01-20 17:08:37.980970
Duration20.59 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

property_type
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size237.0 KiB
flat
2997 
house
964 

Length

Max length5
Median length4
Mean length4.2433729
Min length4

Characters and Unicode

Total characters16808
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowflat
2nd rowflat
3rd rowflat
4th rowhouse
5th rowflat

Common Values

ValueCountFrequency (%)
flat 2997
75.7%
house 964
 
24.3%

Length

2025-01-20T22:38:38.158550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-20T22:38:38.315597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
flat 2997
75.7%
house 964
 
24.3%

Most occurring characters

ValueCountFrequency (%)
f 2997
17.8%
l 2997
17.8%
a 2997
17.8%
t 2997
17.8%
h 964
 
5.7%
o 964
 
5.7%
u 964
 
5.7%
s 964
 
5.7%
e 964
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 16808
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f 2997
17.8%
l 2997
17.8%
a 2997
17.8%
t 2997
17.8%
h 964
 
5.7%
o 964
 
5.7%
u 964
 
5.7%
s 964
 
5.7%
e 964
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 16808
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
f 2997
17.8%
l 2997
17.8%
a 2997
17.8%
t 2997
17.8%
h 964
 
5.7%
o 964
 
5.7%
u 964
 
5.7%
s 964
 
5.7%
e 964
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16808
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 2997
17.8%
l 2997
17.8%
a 2997
17.8%
t 2997
17.8%
h 964
 
5.7%
o 964
 
5.7%
u 964
 
5.7%
s 964
 
5.7%
e 964
 
5.7%
Distinct725
Distinct (%)18.3%
Missing1
Missing (%)< 0.1%
Memory size285.6 KiB
2025-01-20T22:38:38.852886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length49
Median length41
Mean length16.812121
Min length1

Characters and Unicode

Total characters66576
Distinct characters41
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique325 ?
Unique (%)8.2%

Sample

1st rowsare homes
2nd rowireo skyon
3rd rowshapoorji pallonji joyville gurugram
4th rowrattan garden
5th rowsignature global park 4
ValueCountFrequency (%)
independent 574
 
5.6%
the 363
 
3.5%
dlf 229
 
2.2%
park 221
 
2.1%
city 176
 
1.7%
global 167
 
1.6%
signature 161
 
1.6%
emaar 159
 
1.5%
m3m 156
 
1.5%
heights 139
 
1.3%
Other values (817) 7966
77.3%
2025-01-20T22:38:39.796579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 7307
 
11.0%
6353
 
9.5%
a 6253
 
9.4%
n 4605
 
6.9%
r 4460
 
6.7%
i 4107
 
6.2%
t 4017
 
6.0%
s 3706
 
5.6%
l 3129
 
4.7%
o 2929
 
4.4%
Other values (31) 19710
29.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 59624
89.6%
Space Separator 6353
 
9.5%
Decimal Number 573
 
0.9%
Other Punctuation 15
 
< 0.1%
Dash Punctuation 11
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 7307
12.3%
a 6253
 
10.5%
n 4605
 
7.7%
r 4460
 
7.5%
i 4107
 
6.9%
t 4017
 
6.7%
s 3706
 
6.2%
l 3129
 
5.2%
o 2929
 
4.9%
d 2748
 
4.6%
Other values (16) 16363
27.4%
Decimal Number
ValueCountFrequency (%)
3 220
38.4%
2 85
 
14.8%
1 84
 
14.7%
6 63
 
11.0%
8 35
 
6.1%
4 19
 
3.3%
5 19
 
3.3%
7 17
 
3.0%
9 16
 
2.8%
0 15
 
2.6%
Other Punctuation
ValueCountFrequency (%)
, 12
80.0%
/ 2
 
13.3%
. 1
 
6.7%
Space Separator
ValueCountFrequency (%)
6353
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 59624
89.6%
Common 6952
 
10.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 7307
12.3%
a 6253
 
10.5%
n 4605
 
7.7%
r 4460
 
7.5%
i 4107
 
6.9%
t 4017
 
6.7%
s 3706
 
6.2%
l 3129
 
5.2%
o 2929
 
4.9%
d 2748
 
4.6%
Other values (16) 16363
27.4%
Common
ValueCountFrequency (%)
6353
91.4%
3 220
 
3.2%
2 85
 
1.2%
1 84
 
1.2%
6 63
 
0.9%
8 35
 
0.5%
4 19
 
0.3%
5 19
 
0.3%
7 17
 
0.2%
9 16
 
0.2%
Other values (5) 41
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66576
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 7307
 
11.0%
6353
 
9.5%
a 6253
 
9.4%
n 4605
 
6.9%
r 4460
 
6.7%
i 4107
 
6.2%
t 4017
 
6.0%
s 3706
 
5.6%
l 3129
 
4.7%
o 2929
 
4.4%
Other values (31) 19710
29.6%

sector
Text

Distinct234
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size257.5 KiB
2025-01-20T22:38:40.491191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length57
Median length9
Mean length9.5367331
Min length3

Characters and Unicode

Total characters37775
Distinct characters40
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique81 ?
Unique (%)2.0%

Sample

1st rowsector 92
2nd rowsector 60
3rd rowsector 102
4th rowsector 7
5th rowsector 36
ValueCountFrequency (%)
sector 3605
44.7%
road 209
 
2.6%
sohna 183
 
2.3%
102 112
 
1.4%
85 110
 
1.4%
92 105
 
1.3%
69 94
 
1.2%
90 91
 
1.1%
65 90
 
1.1%
2 89
 
1.1%
Other values (265) 3376
41.9%
2025-01-20T22:38:41.310876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4105
10.9%
o 4095
10.8%
r 4030
10.7%
s 3960
10.5%
e 3806
10.1%
c 3722
9.9%
t 3687
9.8%
1 1129
 
3.0%
a 1032
 
2.7%
0 828
 
2.2%
Other values (30) 7381
19.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 26034
68.9%
Decimal Number 7608
 
20.1%
Space Separator 4105
 
10.9%
Other Punctuation 17
 
< 0.1%
Dash Punctuation 11
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 4095
15.7%
r 4030
15.5%
s 3960
15.2%
e 3806
14.6%
c 3722
14.3%
t 3687
14.2%
a 1032
 
4.0%
n 348
 
1.3%
d 330
 
1.3%
h 301
 
1.2%
Other values (15) 723
 
2.8%
Decimal Number
ValueCountFrequency (%)
1 1129
14.8%
0 828
10.9%
8 806
10.6%
9 806
10.6%
6 760
10.0%
2 726
9.5%
7 716
9.4%
3 712
9.4%
5 622
8.2%
4 503
6.6%
Other Punctuation
ValueCountFrequency (%)
, 15
88.2%
/ 1
 
5.9%
' 1
 
5.9%
Space Separator
ValueCountFrequency (%)
4105
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 26034
68.9%
Common 11741
31.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 4095
15.7%
r 4030
15.5%
s 3960
15.2%
e 3806
14.6%
c 3722
14.3%
t 3687
14.2%
a 1032
 
4.0%
n 348
 
1.3%
d 330
 
1.3%
h 301
 
1.2%
Other values (15) 723
 
2.8%
Common
ValueCountFrequency (%)
4105
35.0%
1 1129
 
9.6%
0 828
 
7.1%
8 806
 
6.9%
9 806
 
6.9%
6 760
 
6.5%
2 726
 
6.2%
7 716
 
6.1%
3 712
 
6.1%
5 622
 
5.3%
Other values (5) 531
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37775
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4105
10.9%
o 4095
10.8%
r 4030
10.7%
s 3960
10.5%
e 3806
10.1%
c 3722
9.9%
t 3687
9.8%
1 1129
 
3.0%
a 1032
 
2.7%
0 828
 
2.2%
Other values (30) 7381
19.5%

price
Real number (ℝ)

High correlation 

Distinct479
Distinct (%)12.2%
Missing20
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2.4754986
Minimum0.07
Maximum31.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.1 KiB
2025-01-20T22:38:41.510149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.07
5-th percentile0.36
Q10.9
median1.5
Q32.65
95-th percentile8.4
Maximum31.5
Range31.43
Interquartile range (IQR)1.75

Descriptive statistics

Standard deviation2.9225703
Coefficient of variation (CV)1.1805986
Kurtosis15.480087
Mean2.4754986
Median Absolute Deviation (MAD)0.72
Skewness3.3303638
Sum9755.94
Variance8.5414172
MonotonicityNot monotonic
2025-01-20T22:38:41.711131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.25 84
 
2.1%
1.5 70
 
1.8%
1.1 70
 
1.8%
0.9 70
 
1.8%
1.2 66
 
1.7%
1.3 64
 
1.6%
1.4 64
 
1.6%
0.95 62
 
1.6%
2 60
 
1.5%
1 54
 
1.4%
Other values (469) 3277
82.7%
ValueCountFrequency (%)
0.07 1
 
< 0.1%
0.13 1
 
< 0.1%
0.15 1
 
< 0.1%
0.16 1
 
< 0.1%
0.17 1
 
< 0.1%
0.18 1
 
< 0.1%
0.19 1
 
< 0.1%
0.2 9
0.2%
0.21 6
0.2%
0.22 10
0.3%
ValueCountFrequency (%)
31.5 1
 
< 0.1%
27.5 1
 
< 0.1%
26 2
0.1%
25 1
 
< 0.1%
24 1
 
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
20 3
0.1%
19.5 2
0.1%
19 3
0.1%

price_per_sqft
Real number (ℝ)

High correlation 

Distinct2731
Distinct (%)69.3%
Missing20
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean13915.093
Minimum2
Maximum600000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.1 KiB
2025-01-20T22:38:41.894089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4681
Q16790
median9000
Q313750
95-th percentile33333
Maximum600000
Range599998
Interquartile range (IQR)6960

Descriptive statistics

Standard deviation23181.16
Coefficient of variation (CV)1.6659005
Kurtosis176.40897
Mean13915.093
Median Absolute Deviation (MAD)2781
Skewness11.011874
Sum54839381
Variance5.373662 × 108
MonotonicityNot monotonic
2025-01-20T22:38:42.297153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 29
 
0.7%
8000 21
 
0.5%
5000 18
 
0.5%
12500 18
 
0.5%
11111 17
 
0.4%
8333 15
 
0.4%
6666 15
 
0.4%
7500 14
 
0.4%
22222 14
 
0.4%
6000 12
 
0.3%
Other values (2721) 3768
95.1%
(Missing) 20
 
0.5%
ValueCountFrequency (%)
2 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
7 1
< 0.1%
9 1
< 0.1%
53 1
< 0.1%
57 1
< 0.1%
58 2
0.1%
60 1
< 0.1%
61 1
< 0.1%
ValueCountFrequency (%)
600000 1
< 0.1%
400000 1
< 0.1%
315789 1
< 0.1%
308333 1
< 0.1%
290948 1
< 0.1%
283333 1
< 0.1%
266666 1
< 0.1%
261194 1
< 0.1%
245398 1
< 0.1%
241666 1
< 0.1%

area
Real number (ℝ)

High correlation  Skewed 

Distinct1351
Distinct (%)34.3%
Missing20
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean4647.0256
Minimum45
Maximum7250000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.1 KiB
2025-01-20T22:38:42.496249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum45
5-th percentile500
Q11187
median1700
Q32290
95-th percentile4200
Maximum7250000
Range7249955
Interquartile range (IQR)1103

Descriptive statistics

Standard deviation117585.65
Coefficient of variation (CV)25.303421
Kurtosis3662.5658
Mean4647.0256
Median Absolute Deviation (MAD)550
Skewness59.61688
Sum18313928
Variance1.3826384 × 1010
MonotonicityNot monotonic
2025-01-20T22:38:42.703907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1650 55
 
1.4%
1350 53
 
1.3%
900 53
 
1.3%
1800 50
 
1.3%
3240 45
 
1.1%
1950 44
 
1.1%
2700 40
 
1.0%
2000 37
 
0.9%
2400 25
 
0.6%
2250 25
 
0.6%
Other values (1341) 3514
88.7%
ValueCountFrequency (%)
45 1
 
< 0.1%
50 5
0.1%
54 1
 
< 0.1%
55 1
 
< 0.1%
56 1
 
< 0.1%
57 1
 
< 0.1%
60 2
 
0.1%
61 1
 
< 0.1%
67 2
 
0.1%
70 1
 
< 0.1%
ValueCountFrequency (%)
7250000 1
< 0.1%
875000 1
< 0.1%
642857 1
< 0.1%
620000 1
< 0.1%
566667 1
< 0.1%
215517 1
< 0.1%
98978 1
< 0.1%
82781 1
< 0.1%
65517 2
0.1%
65261 1
< 0.1%
Distinct2443
Distinct (%)61.7%
Missing0
Missing (%)0.0%
Memory size427.1 KiB
2025-01-20T22:38:43.293892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length124
Median length119
Mean length53.375915
Min length12

Characters and Unicode

Total characters211422
Distinct characters35
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1855 ?
Unique (%)46.8%

Sample

1st rowCarpet area: 1200 (111.48 sq.m.)
2nd rowSuper Built up area 2809(260.96 sq.m.)Carpet area: 2400 sq.ft. (222.97 sq.m.)
3rd rowSuper Built up area 915(85.01 sq.m.)
4th rowBuilt Up area: 963 (89.47 sq.m.)
5th rowCarpet area: 1120 (104.05 sq.m.)
ValueCountFrequency (%)
area 5937
18.5%
sq.m 3937
12.3%
up 3167
 
9.9%
built 2438
 
7.6%
super 1934
 
6.0%
sq.ft 1811
 
5.7%
sq.m.)carpet 1239
 
3.9%
plot 767
 
2.4%
carpet 760
 
2.4%
sq.m.)built 727
 
2.3%
Other values (2926) 9298
29.0%
2025-01-20T22:38:44.120263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
28054
 
13.3%
. 21619
 
10.2%
a 14032
 
6.6%
r 10029
 
4.7%
e 9874
 
4.7%
1 9714
 
4.6%
s 8026
 
3.8%
q 7871
 
3.7%
t 7748
 
3.7%
p 7104
 
3.4%
Other values (25) 87351
41.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 87613
41.4%
Decimal Number 50006
23.7%
Space Separator 28054
 
13.3%
Other Punctuation 24855
 
11.8%
Uppercase Letter 9104
 
4.3%
Close Punctuation 5895
 
2.8%
Open Punctuation 5895
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 14032
16.0%
r 10029
11.4%
e 9874
11.3%
s 8026
9.2%
q 7871
9.0%
t 7748
8.8%
p 7104
8.1%
u 7035
8.0%
m 5905
6.7%
l 3934
 
4.5%
Other values (5) 6055
6.9%
Decimal Number
ValueCountFrequency (%)
1 9714
19.4%
0 7087
14.2%
2 6025
12.0%
5 5018
10.0%
3 4186
8.4%
4 3940
7.9%
6 3890
7.8%
7 3445
 
6.9%
8 3368
 
6.7%
9 3333
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
B 3167
34.8%
C 2003
22.0%
S 1934
21.2%
U 1233
 
13.5%
P 767
 
8.4%
Other Punctuation
ValueCountFrequency (%)
. 21619
87.0%
: 3236
 
13.0%
Space Separator
ValueCountFrequency (%)
28054
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5895
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5895
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 114705
54.3%
Latin 96717
45.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 14032
14.5%
r 10029
10.4%
e 9874
10.2%
s 8026
8.3%
q 7871
8.1%
t 7748
8.0%
p 7104
7.3%
u 7035
7.3%
m 5905
 
6.1%
l 3934
 
4.1%
Other values (10) 15159
15.7%
Common
ValueCountFrequency (%)
28054
24.5%
. 21619
18.8%
1 9714
 
8.5%
0 7087
 
6.2%
2 6025
 
5.3%
) 5895
 
5.1%
( 5895
 
5.1%
5 5018
 
4.4%
3 4186
 
3.6%
4 3940
 
3.4%
Other values (5) 17272
15.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 211422
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
28054
 
13.3%
. 21619
 
10.2%
a 14032
 
6.6%
r 10029
 
4.7%
e 9874
 
4.7%
1 9714
 
4.6%
s 8026
 
3.8%
q 7871
 
3.7%
t 7748
 
3.7%
p 7104
 
3.4%
Other values (25) 87351
41.3%

bedRoom
Real number (ℝ)

High correlation 

Distinct21
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3630396
Minimum1
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.1 KiB
2025-01-20T22:38:44.287604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum36
Range35
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.0090671
Coefficient of variation (CV)0.59739621
Kurtosis44.14148
Mean3.3630396
Median Absolute Deviation (MAD)1
Skewness4.754086
Sum13321
Variance4.0363508
MonotonicityNot monotonic
2025-01-20T22:38:44.444246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
3 1583
40.0%
2 1032
26.1%
4 710
17.9%
5 225
 
5.7%
1 142
 
3.6%
6 84
 
2.1%
9 45
 
1.1%
8 33
 
0.8%
7 31
 
0.8%
12 28
 
0.7%
Other values (11) 48
 
1.2%
ValueCountFrequency (%)
1 142
 
3.6%
2 1032
26.1%
3 1583
40.0%
4 710
17.9%
5 225
 
5.7%
6 84
 
2.1%
7 31
 
0.8%
8 33
 
0.8%
9 45
 
1.1%
10 21
 
0.5%
ValueCountFrequency (%)
36 1
 
< 0.1%
34 1
 
< 0.1%
21 1
 
< 0.1%
20 1
 
< 0.1%
19 2
 
0.1%
18 2
 
0.1%
16 12
0.3%
14 1
 
< 0.1%
13 4
 
0.1%
12 28
0.7%

bathroom
Real number (ℝ)

High correlation 

Distinct21
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4069679
Minimum1
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.1 KiB
2025-01-20T22:38:44.612170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum36
Range35
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.0492649
Coefficient of variation (CV)0.60149229
Kurtosis41.523826
Mean3.4069679
Median Absolute Deviation (MAD)1
Skewness4.4927313
Sum13495
Variance4.1994868
MonotonicityNot monotonic
2025-01-20T22:38:44.784798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
2 1158
29.2%
3 1154
29.1%
4 864
21.8%
5 306
 
7.7%
1 177
 
4.5%
6 124
 
3.1%
7 44
 
1.1%
9 42
 
1.1%
8 28
 
0.7%
12 22
 
0.6%
Other values (11) 42
 
1.1%
ValueCountFrequency (%)
1 177
 
4.5%
2 1158
29.2%
3 1154
29.1%
4 864
21.8%
5 306
 
7.7%
6 124
 
3.1%
7 44
 
1.1%
8 28
 
0.7%
9 42
 
1.1%
10 11
 
0.3%
ValueCountFrequency (%)
36 1
 
< 0.1%
34 1
 
< 0.1%
21 1
 
< 0.1%
20 3
 
0.1%
18 4
 
0.1%
17 3
 
0.1%
16 8
 
0.2%
14 2
 
0.1%
13 4
 
0.1%
12 22
0.6%

balcony
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size225.7 KiB
3+
1227 
3
1133 
2
969 
1
420 
0
212 

Length

Max length2
Median length1
Mean length1.3097703
Min length1

Characters and Unicode

Total characters5188
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row2
4th row2
5th row3

Common Values

ValueCountFrequency (%)
3+ 1227
31.0%
3 1133
28.6%
2 969
24.5%
1 420
 
10.6%
0 212
 
5.4%

Length

2025-01-20T22:38:44.961080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-20T22:38:45.143141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 2360
59.6%
2 969
24.5%
1 420
 
10.6%
0 212
 
5.4%

Most occurring characters

ValueCountFrequency (%)
3 2360
45.5%
+ 1227
23.7%
2 969
18.7%
1 420
 
8.1%
0 212
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3961
76.3%
Math Symbol 1227
 
23.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 2360
59.6%
2 969
24.5%
1 420
 
10.6%
0 212
 
5.4%
Math Symbol
ValueCountFrequency (%)
+ 1227
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5188
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 2360
45.5%
+ 1227
23.7%
2 969
18.7%
1 420
 
8.1%
0 212
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5188
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 2360
45.5%
+ 1227
23.7%
2 969
18.7%
1 420
 
8.1%
0 212
 
4.1%

floorNum
Real number (ℝ)

Zeros 

Distinct44
Distinct (%)1.1%
Missing21
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean6.6677665
Minimum0
Maximum51
Zeros139
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size31.1 KiB
2025-01-20T22:38:45.325389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q310
95-th percentile18
Maximum51
Range51
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.0056694
Coefficient of variation (CV)0.90070181
Kurtosis4.8099619
Mean6.6677665
Median Absolute Deviation (MAD)3
Skewness1.7524954
Sum26271
Variance36.068065
MonotonicityNot monotonic
2025-01-20T22:38:45.542276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
3 552
13.9%
2 547
13.8%
1 404
10.2%
4 339
 
8.6%
8 200
 
5.0%
7 188
 
4.7%
6 188
 
4.7%
10 187
 
4.7%
5 181
 
4.6%
9 171
 
4.3%
Other values (34) 983
24.8%
ValueCountFrequency (%)
0 139
 
3.5%
1 404
10.2%
2 547
13.8%
3 552
13.9%
4 339
8.6%
5 181
 
4.6%
6 188
 
4.7%
7 188
 
4.7%
8 200
 
5.0%
9 171
 
4.3%
ValueCountFrequency (%)
51 1
< 0.1%
45 1
< 0.1%
44 1
< 0.1%
43 2
0.1%
41 1
< 0.1%
40 2
0.1%
39 2
0.1%
38 1
< 0.1%
35 2
0.1%
34 2
0.1%

facing
Categorical

High correlation  Missing 

Distinct8
Distinct (%)0.3%
Missing1177
Missing (%)29.7%
Memory size210.5 KiB
East
668 
North-East
663 
North
410 
West
258 
South
237 
Other values (3)
548 

Length

Max length10
Median length5
Mean length6.8423132
Min length4

Characters and Unicode

Total characters19049
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorth-East
2nd rowEast
3rd rowEast
4th rowSouth
5th rowEast

Common Values

ValueCountFrequency (%)
East 668
16.9%
North-East 663
16.7%
North 410
 
10.4%
West 258
 
6.5%
South 237
 
6.0%
North-West 206
 
5.2%
South-East 183
 
4.6%
South-West 159
 
4.0%
(Missing) 1177
29.7%

Length

2025-01-20T22:38:45.743552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-20T22:38:45.944952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
east 668
24.0%
north-east 663
23.8%
north 410
14.7%
west 258
 
9.3%
south 237
 
8.5%
north-west 206
 
7.4%
south-east 183
 
6.6%
south-west 159
 
5.7%

Most occurring characters

ValueCountFrequency (%)
t 3995
21.0%
s 2137
11.2%
o 1858
9.8%
h 1858
9.8%
E 1514
 
7.9%
a 1514
 
7.9%
N 1279
 
6.7%
r 1279
 
6.7%
- 1211
 
6.4%
W 623
 
3.3%
Other values (3) 1781
9.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13843
72.7%
Uppercase Letter 3995
 
21.0%
Dash Punctuation 1211
 
6.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 3995
28.9%
s 2137
15.4%
o 1858
13.4%
h 1858
13.4%
a 1514
 
10.9%
r 1279
 
9.2%
e 623
 
4.5%
u 579
 
4.2%
Uppercase Letter
ValueCountFrequency (%)
E 1514
37.9%
N 1279
32.0%
W 623
15.6%
S 579
 
14.5%
Dash Punctuation
ValueCountFrequency (%)
- 1211
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 17838
93.6%
Common 1211
 
6.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 3995
22.4%
s 2137
12.0%
o 1858
10.4%
h 1858
10.4%
E 1514
 
8.5%
a 1514
 
8.5%
N 1279
 
7.2%
r 1279
 
7.2%
W 623
 
3.5%
e 623
 
3.5%
Other values (2) 1158
 
6.5%
Common
ValueCountFrequency (%)
- 1211
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19049
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 3995
21.0%
s 2137
11.2%
o 1858
9.8%
h 1858
9.8%
E 1514
 
7.9%
a 1514
 
7.9%
N 1279
 
6.7%
r 1279
 
6.7%
- 1211
 
6.4%
W 623
 
3.3%
Other values (3) 1781
9.3%

agePossession
Categorical

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size270.8 KiB
Relatively New
1705 
New Property
647 
Moderately Old
610 
Undefined
512 
Old Property
353 

Length

Max length18
Median length14
Mean length12.984095
Min length9

Characters and Unicode

Total characters51430
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowModerately Old
2nd rowRelatively New
3rd rowRelatively New
4th rowOld Property
5th rowUndefined

Common Values

ValueCountFrequency (%)
Relatively New 1705
43.0%
New Property 647
 
16.3%
Moderately Old 610
 
15.4%
Undefined 512
 
12.9%
Old Property 353
 
8.9%
Under Construction 134
 
3.4%

Length

2025-01-20T22:38:46.144315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-20T22:38:46.291725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
new 2352
31.7%
relatively 1705
23.0%
property 1000
13.5%
old 963
13.0%
moderately 610
 
8.2%
undefined 512
 
6.9%
under 134
 
1.8%
construction 134
 
1.8%

Most occurring characters

ValueCountFrequency (%)
e 9140
17.8%
l 4983
 
9.7%
t 3583
 
7.0%
3449
 
6.7%
y 3315
 
6.4%
r 2878
 
5.6%
d 2731
 
5.3%
N 2352
 
4.6%
w 2352
 
4.6%
i 2351
 
4.6%
Other values (15) 14296
27.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 40571
78.9%
Uppercase Letter 7410
 
14.4%
Space Separator 3449
 
6.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 9140
22.5%
l 4983
12.3%
t 3583
 
8.8%
y 3315
 
8.2%
r 2878
 
7.1%
d 2731
 
6.7%
w 2352
 
5.8%
i 2351
 
5.8%
a 2315
 
5.7%
o 1878
 
4.6%
Other values (7) 5045
12.4%
Uppercase Letter
ValueCountFrequency (%)
N 2352
31.7%
R 1705
23.0%
P 1000
13.5%
O 963
13.0%
U 646
 
8.7%
M 610
 
8.2%
C 134
 
1.8%
Space Separator
ValueCountFrequency (%)
3449
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 47981
93.3%
Common 3449
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 9140
19.0%
l 4983
10.4%
t 3583
 
7.5%
y 3315
 
6.9%
r 2878
 
6.0%
d 2731
 
5.7%
N 2352
 
4.9%
w 2352
 
4.9%
i 2351
 
4.9%
a 2315
 
4.8%
Other values (14) 11981
25.0%
Common
ValueCountFrequency (%)
3449
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 9140
17.8%
l 4983
 
9.7%
t 3583
 
7.0%
3449
 
6.7%
y 3315
 
6.4%
r 2878
 
5.6%
d 2731
 
5.3%
N 2352
 
4.6%
w 2352
 
4.6%
i 2351
 
4.6%
Other values (15) 14296
27.8%

super_built_up_area
Real number (ℝ)

High correlation  Missing 

Distinct599
Distinct (%)31.0%
Missing2027
Missing (%)51.2%
Infinite0
Infinite (%)0.0%
Mean1917.7599
Minimum89
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.1 KiB
2025-01-20T22:38:46.493311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum89
5-th percentile748.25
Q11457
median1825
Q32215
95-th percentile3187.45
Maximum10000
Range9911
Interquartile range (IQR)758

Descriptive statistics

Standard deviation768.23467
Coefficient of variation (CV)0.40058961
Kurtosis9.9468317
Mean1917.7599
Median Absolute Deviation (MAD)375
Skewness1.8053184
Sum3708947.5
Variance590184.51
MonotonicityNot monotonic
2025-01-20T22:38:46.707786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1650 38
 
1.0%
1950 38
 
1.0%
2000 26
 
0.7%
1578 25
 
0.6%
2150 23
 
0.6%
1640 22
 
0.6%
2408 20
 
0.5%
1900 19
 
0.5%
1350 19
 
0.5%
1930 18
 
0.5%
Other values (589) 1686
42.6%
(Missing) 2027
51.2%
ValueCountFrequency (%)
89 1
< 0.1%
145 1
< 0.1%
161 1
< 0.1%
215 1
< 0.1%
216 1
< 0.1%
325 1
< 0.1%
340 1
< 0.1%
352 1
< 0.1%
380 1
< 0.1%
406 1
< 0.1%
ValueCountFrequency (%)
10000 1
< 0.1%
6926 1
< 0.1%
6000 1
< 0.1%
5800 2
0.1%
5514 1
< 0.1%
5350 2
0.1%
5200 2
0.1%
4890 1
< 0.1%
4857 2
0.1%
4848 2
0.1%

built_up_area
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct656
Distinct (%)35.5%
Missing2113
Missing (%)53.3%
Infinite0
Infinite (%)0.0%
Mean1793.0741
Minimum2
Maximum737147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.1 KiB
2025-01-20T22:38:46.904401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile115.7
Q1360
median1200
Q31861.25
95-th percentile3843.35
Maximum737147
Range737145
Interquartile range (IQR)1501.25

Descriptive statistics

Standard deviation17175.835
Coefficient of variation (CV)9.5789878
Kurtosis1821.9777
Mean1793.0741
Median Absolute Deviation (MAD)750
Skewness42.536442
Sum3313601
Variance2.9500931 × 108
MonotonicityNot monotonic
2025-01-20T22:38:47.096184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
360 46
 
1.2%
1900 35
 
0.9%
300 35
 
0.9%
2000 26
 
0.7%
1600 26
 
0.7%
900 26
 
0.7%
200 25
 
0.6%
1300 25
 
0.6%
1700 24
 
0.6%
500 23
 
0.6%
Other values (646) 1557
39.3%
(Missing) 2113
53.3%
ValueCountFrequency (%)
2 1
 
< 0.1%
14 1
 
< 0.1%
30 1
 
< 0.1%
33 1
 
< 0.1%
40 4
0.1%
45 1
 
< 0.1%
50 9
0.2%
51 1
 
< 0.1%
52.5 1
 
< 0.1%
53 1
 
< 0.1%
ValueCountFrequency (%)
737147 1
 
< 0.1%
26000 1
 
< 0.1%
12000 1
 
< 0.1%
9500 1
 
< 0.1%
9000 4
0.1%
8286 1
 
< 0.1%
8260 1
 
< 0.1%
8000 1
 
< 0.1%
7500 2
0.1%
7450 1
 
< 0.1%

carpet_area
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct744
Distinct (%)37.1%
Missing1958
Missing (%)49.4%
Infinite0
Infinite (%)0.0%
Mean2445.2755
Minimum15
Maximum607936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.1 KiB
2025-01-20T22:38:47.293116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile337.11906
Q1802
median1264
Q31782
95-th percentile2947.2
Maximum607936
Range607921
Interquartile range (IQR)980

Descriptive statistics

Standard deviation22044.807
Coefficient of variation (CV)9.0152651
Kurtosis646.90592
Mean2445.2755
Median Absolute Deviation (MAD)486
Skewness25.168213
Sum4897886.9
Variance4.8597352 × 108
MonotonicityNot monotonic
2025-01-20T22:38:47.495011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1400 42
 
1.1%
1800 37
 
0.9%
1600 36
 
0.9%
1200 33
 
0.8%
1500 30
 
0.8%
1350 29
 
0.7%
1650 28
 
0.7%
900 25
 
0.6%
1300 23
 
0.6%
2000 23
 
0.6%
Other values (734) 1697
42.8%
(Missing) 1958
49.4%
ValueCountFrequency (%)
15 1
 
< 0.1%
33 1
 
< 0.1%
48 1
 
< 0.1%
50 1
 
< 0.1%
59 1
 
< 0.1%
60 2
0.1%
66 1
 
< 0.1%
72 1
 
< 0.1%
76.44 3
0.1%
77.31 2
0.1%
ValueCountFrequency (%)
607936 1
< 0.1%
569243 1
< 0.1%
514396 1
< 0.1%
64529 1
< 0.1%
64412 1
< 0.1%
58141 1
< 0.1%
54917 1
< 0.1%
48811 1
< 0.1%
45966 1
< 0.1%
34401 1
< 0.1%

study room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size224.5 KiB
0
3224 
1
737 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3961
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3224
81.4%
1 737
 
18.6%

Length

2025-01-20T22:38:47.674011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-20T22:38:47.811371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3224
81.4%
1 737
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 3224
81.4%
1 737
 
18.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3961
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3224
81.4%
1 737
 
18.6%

Most occurring scripts

ValueCountFrequency (%)
Common 3961
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3224
81.4%
1 737
 
18.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3961
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3224
81.4%
1 737
 
18.6%

servant room
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size224.5 KiB
0
2584 
1
1377 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3961
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2584
65.2%
1 1377
34.8%

Length

2025-01-20T22:38:47.957699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-20T22:38:48.094551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2584
65.2%
1 1377
34.8%

Most occurring characters

ValueCountFrequency (%)
0 2584
65.2%
1 1377
34.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3961
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2584
65.2%
1 1377
34.8%

Most occurring scripts

ValueCountFrequency (%)
Common 3961
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2584
65.2%
1 1377
34.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3961
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2584
65.2%
1 1377
34.8%

store room
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size224.5 KiB
0
3600 
1
361 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3961
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3600
90.9%
1 361
 
9.1%

Length

2025-01-20T22:38:48.236077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-20T22:38:48.359247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3600
90.9%
1 361
 
9.1%

Most occurring characters

ValueCountFrequency (%)
0 3600
90.9%
1 361
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3961
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3600
90.9%
1 361
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
Common 3961
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3600
90.9%
1 361
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3961
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3600
90.9%
1 361
 
9.1%

pooja room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size224.5 KiB
0
3275 
1
686 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3961
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3275
82.7%
1 686
 
17.3%

Length

2025-01-20T22:38:48.505472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-20T22:38:48.642339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3275
82.7%
1 686
 
17.3%

Most occurring characters

ValueCountFrequency (%)
0 3275
82.7%
1 686
 
17.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3961
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3275
82.7%
1 686
 
17.3%

Most occurring scripts

ValueCountFrequency (%)
Common 3961
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3275
82.7%
1 686
 
17.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3961
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3275
82.7%
1 686
 
17.3%

others
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size224.5 KiB
0
3522 
1
439 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3961
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3522
88.9%
1 439
 
11.1%

Length

2025-01-20T22:38:48.793836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-20T22:38:48.933018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3522
88.9%
1 439
 
11.1%

Most occurring characters

ValueCountFrequency (%)
0 3522
88.9%
1 439
 
11.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3961
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3522
88.9%
1 439
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Common 3961
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3522
88.9%
1 439
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3961
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3522
88.9%
1 439
 
11.1%

furnishing_type
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size224.5 KiB
1
2638 
0
1102 
2
 
221

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3961
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 2638
66.6%
0 1102
27.8%
2 221
 
5.6%

Length

2025-01-20T22:38:49.077941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-20T22:38:49.209708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 2638
66.6%
0 1102
27.8%
2 221
 
5.6%

Most occurring characters

ValueCountFrequency (%)
1 2638
66.6%
0 1102
27.8%
2 221
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3961
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2638
66.6%
0 1102
27.8%
2 221
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Common 3961
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2638
66.6%
0 1102
27.8%
2 221
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3961
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2638
66.6%
0 1102
27.8%
2 221
 
5.6%

luxury_score
Real number (ℝ)

Zeros 

Distinct161
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.105781
Minimum0
Maximum174
Zeros559
Zeros (%)14.1%
Negative0
Negative (%)0.0%
Memory size31.1 KiB
2025-01-20T22:38:49.374183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q128
median56
Q3108
95-th percentile168
Maximum174
Range174
Interquartile range (IQR)80

Descriptive statistics

Standard deviation53.041219
Coefficient of variation (CV)0.76753663
Kurtosis-0.83249815
Mean69.105781
Median Absolute Deviation (MAD)39
Skewness0.49839736
Sum273728
Variance2813.3709
MonotonicityNot monotonic
2025-01-20T22:38:49.573188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 559
 
14.1%
49 358
 
9.0%
174 196
 
4.9%
44 64
 
1.6%
38 58
 
1.5%
165 56
 
1.4%
72 56
 
1.4%
7 52
 
1.3%
60 50
 
1.3%
37 49
 
1.2%
Other values (151) 2463
62.2%
ValueCountFrequency (%)
0 559
14.1%
5 6
 
0.2%
6 6
 
0.2%
7 52
 
1.3%
8 34
 
0.9%
9 10
 
0.3%
12 10
 
0.3%
13 11
 
0.3%
14 13
 
0.3%
15 46
 
1.2%
ValueCountFrequency (%)
174 196
4.9%
169 1
 
< 0.1%
168 9
 
0.2%
167 21
 
0.5%
166 11
 
0.3%
165 56
 
1.4%
161 3
 
0.1%
160 30
 
0.8%
159 23
 
0.6%
158 34
 
0.9%

Interactions

2025-01-20T22:38:35.429597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:21.504092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:23.464843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:25.063414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:26.523522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:28.050863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:29.586176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:31.034277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:32.592831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:34.030798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:35.569801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:21.752697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:23.660457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:25.203074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:26.665914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:28.211137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:29.748935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:31.163259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:32.737390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:34.161778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:35.711880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:21.908991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:23.852299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:25.347671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:26.806788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:28.379289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:29.901406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:31.312527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:32.883780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:34.302247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:35.857825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:22.082901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:24.022285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:25.498916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:26.955277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:28.547084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:30.030030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:31.447964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:33.035092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:34.453421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:36.002470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:22.249082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:24.168499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:25.650481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:27.098141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:28.679111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:30.185108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:31.600794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:33.184597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:34.600101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:36.144595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:22.419778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:24.331356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:25.800901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:27.241234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:28.844904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:30.334734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:31.728305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:33.331003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:34.748401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:36.280097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:22.666507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:24.481075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:25.932816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:27.382471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:28.983184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:30.466433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:32.050388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:33.462160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:34.876656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:36.414892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:22.883617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:24.599520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:26.081599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:27.557534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:29.111815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:30.598337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:32.180406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:33.592707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:35.015491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:36.559717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:23.104747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:24.761775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:26.229408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:27.716154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:29.292701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:30.745839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:32.293695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:33.726751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:35.151042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:36.711006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:23.305198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:24.908001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:26.373983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:27.886497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:29.436594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:30.885098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:32.447070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:33.872660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-20T22:38:35.276066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2025-01-20T22:38:49.742979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
agePossessionareabalconybathroombedRoombuilt_up_areacarpet_areafacingfloorNumfurnishing_typeluxury_scoreotherspooja roompriceprice_per_sqftproperty_typeservant roomstore roomstudy roomsuper_built_up_area
agePossession1.0000.0000.2210.0750.1140.0000.0000.0920.1240.2170.2240.1050.1860.0940.0510.3600.2860.1490.1140.084
area0.0001.0000.0000.6830.6120.4160.8060.0370.1270.0420.2740.0000.0000.7480.1960.0190.0000.0000.0000.950
balcony0.2210.0001.0000.1620.1130.0000.0220.0000.0840.1850.2240.0770.1900.1370.0370.2160.4380.1370.1870.305
bathroom0.0750.6830.1621.0000.8550.0080.5990.0350.0060.1600.1860.0660.2650.7180.4030.4240.3060.1740.1610.821
bedRoom0.1140.6120.1130.8551.000-0.1240.5670.034-0.0970.1330.0540.0640.2660.6710.4060.6050.1330.2010.1660.801
built_up_area0.0000.4160.0000.008-0.1241.0000.9681.0000.3440.0860.2690.0000.0000.039-0.3830.0000.0000.0000.0000.927
carpet_area0.0000.8060.0220.5990.5670.9681.0000.0000.1570.0000.2420.0160.0000.6200.1370.0000.0000.0000.0090.896
facing0.0920.0370.0000.0350.0341.0000.0001.0000.0000.0480.0620.0000.0160.0230.0000.0880.0360.0390.0000.000
floorNum0.1240.1270.0840.006-0.0970.3440.1570.0001.0000.0230.2430.0290.0970.018-0.1110.4850.0890.1070.0710.154
furnishing_type0.2170.0420.1850.1600.1330.0860.0000.0480.0231.0000.2540.0520.2230.1780.0000.0660.2820.1640.1520.136
luxury_score0.2240.2740.2240.1860.0540.2690.2420.0620.2430.2541.0000.1680.1940.2310.0620.3430.3590.2270.1930.230
others0.1050.0000.0770.0660.0640.0000.0160.0000.0290.0520.1681.0000.0380.0330.0290.0290.0000.1040.0400.086
pooja room0.1860.0000.1900.2650.2660.0000.0000.0160.0970.2230.1940.0381.0000.3320.0320.2430.2560.3030.3220.156
price0.0940.7480.1370.7180.6710.0390.6200.0230.0180.1780.2310.0330.3321.0000.7330.5070.3720.2970.2420.776
price_per_sqft0.0510.1960.0370.4030.406-0.3830.1370.000-0.1110.0000.0620.0290.0320.7331.0000.2060.0430.0000.0330.290
property_type0.3600.0190.2160.4240.6050.0000.0000.0880.4850.0660.3430.0290.2430.5070.2061.0000.0490.2340.1141.000
servant room0.2860.0000.4380.3060.1330.0000.0000.0360.0890.2820.3590.0000.2560.3720.0430.0491.0000.1610.1900.587
store room0.1490.0000.1370.1740.2010.0000.0000.0390.1070.1640.2270.1040.3030.2970.0000.2340.1611.0000.2170.039
study room0.1140.0000.1870.1610.1660.0000.0090.0000.0710.1520.1930.0400.3220.2420.0330.1140.1900.2171.0000.119
super_built_up_area0.0840.9500.3050.8210.8010.9270.8960.0000.1540.1360.2300.0860.1560.7760.2901.0000.5870.0390.1191.000

Missing values

2025-01-20T22:38:36.944784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-20T22:38:37.479003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-01-20T22:38:37.792727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

property_typesocietysectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score
0flatsare homessector 920.605000.01200.0Carpet area: 1200 (111.48 sq.m.)3234.0NaNModerately OldNaNNaN1200.000000049
1flatireo skyonsector 603.6012815.02809.0Super Built up area 2809(260.96 sq.m.)Carpet area: 2400 sq.ft. (222.97 sq.m.)45324.0North-EastRelatively New2809.0NaN2400.001000049
2flatshapoorji pallonji joyville gurugramsector 1020.9710601.0915.0Super Built up area 915(85.01 sq.m.)22212.0EastRelatively New915.0NaNNaN000001100
3houserattan gardensector 71.3013499.0963.0Built Up area: 963 (89.47 sq.m.)5323.0NaNOld PropertyNaN963.0NaN0000010
4flatsignature global park 4sector 360.726428.01120.0Carpet area: 1120 (104.05 sq.m.)3232.0NaNUndefinedNaNNaN1120.00000010
5flatumang winter hillssector 771.055762.01822.0Super Built up area 1822(169.27 sq.m.)Built Up area: 1600 sq.ft. (148.64 sq.m.)Carpet area: 1400 sq.ft. (130.06 sq.m.)3337.0EastRelatively New1822.01600.01400.000000149
6flatvatika gurgaonsector 831.126455.01735.0Super Built up area 1735(161.19 sq.m.)Built Up area: 1500 sq.ft. (139.35 sq.m.)Carpet area: 1400 sq.ft. (130.06 sq.m.)33217.0SouthModerately Old1735.01500.01400.0000000111
7flatm3m antalya hillssector 791.157496.01534.0Super Built up area 1534(142.51 sq.m.)Built Up area: 1200 sq.ft. (111.48 sq.m.)Carpet area: 1103 sq.ft. (102.47 sq.m.)333+1.0NaNUndefined1534.01200.01103.010000151
8flatsupertech huessector 680.907627.01180.0Carpet area: 1180 (109.63 sq.m.)2221.0EastNew PropertyNaNNaN1180.000000144
9flateldeco accoladesohna road0.956178.01538.0Super Built up area 1457(135.36 sq.m.)Built Up area: 1050 sq.ft. (97.55 sq.m.)Carpet area: 849 sq.ft. (78.87 sq.m.)223+8.0SouthUnder Construction1457.01050.0849.010001172
property_typesocietysectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score
3951flatemaar gurgaon greenssector 1021.408484.01650.0Super Built up area 1650(153.29 sq.m.)Built Up area: 1300 sq.ft. (120.77 sq.m.)Carpet area: 1022.58 sq.ft. (95 sq.m.)33310.0EastRelatively New1650.01300.01022.58000001000146
3952flattulip violetsector 691.808955.02010.0Super Built up area 2010(186.74 sq.m.)4429.0South-WestRelatively New2010.0NaNNaN000100165
3953flatdlf the skycourtsector 861.507776.01929.0Super Built up area 1929(179.21 sq.m.)Built Up area: 1550 sq.ft. (144 sq.m.)Carpet area: 1330 sq.ft. (123.56 sq.m.)33115.0South-EastRelatively New1929.01550.01330.000000000001159
3954flatemaar mgf emerald floors premiersector 652.3614303.01650.0Super Built up area 1650(153.29 sq.m.)3330.0North-EastRelatively New1650.0NaNNaN110000159
3955houseindependentsector 1050.378222.0450.0Plot area 450(41.81 sq.m.)2211.0NaNModerately OldNaN450.0NaN0000110
3956flatrof anandasector 950.205599.0357.0Carpet area: 366.08 (34.01 sq.m.)1112.0South-WestRelatively NewNaNNaN366.08023900010153
3957houseindependentshivji park colony0.4910378.0472.0Plot area 530(49.24 sq.m.)2101.0NaNOld PropertyNaN530.0NaN0000017
3958flatexperion windchantssector 1123.6511428.03194.0Built Up area: 2800 (260.13 sq.m.)3523.0South-WestRelatively NewNaN2800.0NaN01000172
3959houseindependentdhankot0.709722.0720.0Plot area 80(66.89 sq.m.)4323.0WestRelatively NewNaN80.0NaN00110112
3960houseeros rosewood citysector 494.0520769.01950.0Plot area 1710(158.86 sq.m.)Built Up area: 2000 sq.ft. (185.81 sq.m.)Carpet area: 1950 sq.ft. (181.16 sq.m.)3332.0EastModerately OldNaN2000.01950.00000001100146

Duplicate rows

Most frequently occurring

property_typesocietysectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score# duplicates
0flatambience caitrionasector 2414.00200000.0700.0Built Up area: 700 (65.03 sq.m.)4533.0EastUndefinedNaN700.0NaN00000102
1flatansal heights 86sector 860.905325.01690.0Built Up area: 1690 (157.01 sq.m.)33210.0NaNNew PropertyNaN1690.0NaN000001292
2flatansal heights 86sector 861.304666.02786.0Super Built up area 2786(258.83 sq.m.)46211.0EastNew Property2786.0NaNNaN010011862
3flatansal housing highland parksector 1030.886429.01369.0Super Built up area 1361(126.44 sq.m.)2233.0NaNNew Property1361.0NaNNaN000001522
4flatantriksh heightssector 840.855556.01530.0Super Built up area 1350(125.42 sq.m.)22310.0North-WestNew Property1350.0NaNNaN100011242
5flatapartmentsector 920.754687.01600.0Carpet area: 1600 (148.64 sq.m.)3432.0EastModerately OldNaNNaN1600.01000011132
6flatashiana anmolsohna road0.8811125.0791.0Super Built up area 1275(118.45 sq.m.)Carpet area: 791 sq.ft. (73.49 sq.m.)22213.0EastRelatively New1275.0NaN791.00000021272
7flatassotech blithsector 990.926739.01365.0Super Built up area 1365(126.81 sq.m.)223+22.0NaNUnder Construction1365.0NaNNaN000001562
8flatassotech blithsector 991.906702.02835.0Built Up area: 2835 (263.38 sq.m.)443+2.0North-EastUndefinedNaN2835.0NaN000001512
9flatats tourmalinesector 1092.308897.02585.0Super Built up area 2585(240.15 sq.m.)343+10.0EastNew Property2585.0NaNNaN010011742